Publications & Presentations

Selected Peer-Reviewed Publications

1. Thal, D., Forrow, L.V., Lipman, E.R., Starling, J.E., Finucane, M. Aggregate Bayesian Causal Forests: The ABCs of Flexible Causal Inference for Hierarchically Structured Data. Bayesian Analysis, under review.
2. Deshpande, S.K., Bai, R., Balocchi, C., Starling, J.E., Weiss, J. VCBART: Bayesian Trees for Varying Coefficients. Bayesian Analysis, 2024.
3. Pendl-Robinson, E., Shao, C., Starling, J.E.. Mitigating Bias to Improve Fairness in Predictive Risk Modeling Using Healthcare Data: An Analysis of Long COVID Risk. Mathematica Data Innovation Lab, 2024.
4. Starling, J.E., Deke, J. Assessing design and analysis considerations for increasing statistical power in subgroup analysis. CMS, 2023.
5. Starling, J.E., Murray, J.S., Carvalho, C.M., Scott, J.G. Targeted Smooth Bayesian Causal Forests. Annals of Applied Statistics, 2021.
6. Starling, J.E., Murray, J.S., Carvalho, C.M., Bukowski, R., Scott, J.G. BART with Targeted Smoothing: An Analysis of Patient-Specific Stillbirth Risk. Annals of Applied Statistics, 2020.

Software Contributions

• tsBART — Bayesian Targeted Smoothing
• tsBCF — Bayesian Causal Forests with Targeted Smoothing
• Stillbirth Risk Prediction Dashboard
• 19andMe COVID-19 Risk Score Calculator

Selected Invited Talks

• “Introduction to Bayesian Analysis.” Mathematica Statistical Workgroup, Feb 2025.
• “Assessing the Assessment: Reinterpreting NAEP Scores Using Hierarchical Bayesian Methods.” FCSM Conference, Oct 2024.
• “Identifying Primary Care Practices with Exemplar Response Using Bayesian Causal Forests.” UMMC Seminar, 2022.